IEEE Trans Biomed Eng. 2021 May;68(5):1726-1736. doi: 10.1109/TBME.2020.3034632. Epub 2021 Apr 21.
Sweat secretions lead to variations in skin conductance (SC) signal. The relatively fast variation of SC, called the phasic component, reflects sympathetic nervous system activity. The slow variation related to thermoregulation and general arousal is known as the tonic component. It is challenging to decompose the SC signal into its constituents to decipher the encoded neural information related to emotional arousal.
We model the phasic component using a second-order differential equation representing the diffusion and evaporation processes of sweating. We include a sparse impulsive neural signal that stimulates the sweat glands for sweat production. We model the tonic component with several cubic B-spline functions. We formulate an optimization problem with physiological priors on system parameters, a sparsity prior on the neural stimuli, and a smoothness prior on the tonic component. Finally, we employ a generalized-cross-validation-based coordinate descent approach to balance among the smoothness of the tonic component, the sparsity of the neural stimuli, and the residual.
We illustrate that we can successfully recover the unknowns separating both tonic and phasic components from both experimental and simulated data (with ). Further, we successfully demonstrate our ability to automatically identify the sparsity level for the neural stimuli and the smoothness level for the tonic component.
Our generalized-cross-validation-based novel method for SC signal decomposition successfully addresses previous challenges and retrieves a physiologically plausible solution.
Accurate decomposition of SC could potentially improve cognitive stress tracking in patients with mental disorders.
汗液分泌会导致皮肤电导（SC）信号发生变化。SC 的相对快速变化，称为相位成分，反映了交感神经系统的活动。与体温调节和一般觉醒相关的缓慢变化称为紧张成分。将 SC 信号分解为组成部分以解读与情绪唤醒相关的编码神经信息具有挑战性。
我们使用代表出汗扩散和蒸发过程的二阶微分方程来模拟相位分量。我们包括一个稀疏脉冲神经信号，该信号刺激汗腺以产生汗液。我们使用几个三次 B 样条函数来模拟紧张成分。我们在系统参数上具有生理先验、在神经刺激上具有稀疏先验以及在紧张成分上具有平滑先验的优化问题。最后，我们采用基于广义交叉验证的坐标下降方法来平衡紧张成分的平滑度、神经刺激的稀疏度和残差。
我们表明，我们可以从实验和模拟数据（具有 ）中成功恢复分离紧张和相位分量的未知因素。此外，我们成功地证明了我们能够自动识别神经刺激的稀疏水平和紧张成分的平滑水平的能力。
我们基于广义交叉验证的 SC 信号分解新方法成功解决了先前的挑战，并提供了一个生理上合理的解决方案。
SC 的准确分解有可能改善精神障碍患者的认知应激跟踪。
